On the use of sensitivity analysis in model reduction to predict flows for varying inflow conditions

  1. Get@NRC: On the use of sensitivity analysis in model reduction to predict flows for varying inflow conditions (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1002/fld.2512
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Journal titleInternational Journal for Numerical Methods in Fluids
Pages122134; # of pages: 13
Subjectreduced order modeling; proper orthogonal decomposition; sensitivity analysis; Navier–Stokes equations
AbstractThe proper orthogonal decomposition (POD)-based model reduction method is more and more successfully used in fluid flows. However, the main drawback of this methodology rests in the robustness of these reduced order models (ROMs) beyond the reference at which POD modes have been derived. Any variation in the flow or shape parameters within the ROM fails to predict the correct dynamics of the flow field. To broaden the spectrum of these models, the POD modes should have the global characteristics of the flow field over which the predictions are required. Mixing of snapshots with varying parameters is one way to improve the global nature of the modes but is computationally demanding because it requires full-order solutions for a number of parameter values in order to assemble atextitrich enough database on which to perform POD. Instead, we have used sensitivity analysis (SA) to include the flow and shape parameters influence during the basis selection process to develop more robust ROMs for varying viscosity (Reynolds number), changing orientation and shape definition of bodies. This study aims at extending these ideas to inflow conditions to demonstrate the effectiveness of the proposed approach in capturing the effect of varying inflow on the dynamics of the flow over an elliptic cylinder. Numerical experiments show that the newly derived models allow for a more accurate representation of the flows when exploring the parameter space.
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AffiliationNRC Industrial Materials Institute; National Research Council Canada
Peer reviewedYes
NPARC number21268345
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Record identifieree403403-7546-4a40-b07a-e24691a92230
Record created2013-06-28
Record modified2016-05-09
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